A deep learning tool without muscle-by-muscle grading to differentiate myositis from facio-scapulo-humeral dystrophy using MRI.

Journal: Diagnostic and interventional imaging
Published Date: Jan 1, 2022

Abstract

PURPOSE: The purpose of this study was to assess the capabilities of a deep learning (DL) tool to discriminate between type 1 facioscapulo-humeral dystrophy (FSHD1) and myositis using whole-body muscle magnetic resonance imaging (MRI) examination without the need for visual grading of muscle signal changes.

Authors

  • Vincent Fabry
    Department of Neurology, Toulouse University Hospital, 31059 Toulouse, France. Electronic address: fabry.v@chu-toulouse.fr.
  • Franck Mamalet
    IRT-Saint Exupéry, 31400 Toulouse, France.
  • Anne Laforet
    Department of Neurology, Toulouse University Hospital, 31059 Toulouse, France.
  • Mikael Capelle
    IRT-Saint Exupéry, 31400 Toulouse, France.
  • Blandine Acket
    Department of Neurology, Toulouse University Hospital, 31059 Toulouse, France.
  • Coralie Sengenes
    StromaLab, Bâtiment INCERE, 31100 Toulouse, France.
  • Pascal Cintas
    Department of Neurology, Toulouse University Hospital, 31059 Toulouse, France.
  • Marie Faruch-Bilfeld
    Department of Radiology, Toulouse University Hospital, 31059 Toulouse, Cedex 9, France.

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